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The MAL has launched a study entitled “EMG modules as a novel biomarker of basal ganglia plasticity in Parkinson’s disease”. The study funded by the Michael J. Fox Foundation aims to identify specific characteristics of muscle activity that could be linked to changes within the brain that are associated with Parkinson’s disease.

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Estimating Patient’s Anatomical Joint Angles with ARMEO Spring

The ArmeoSpring system is an ergonomic arm exoskeleton with six instrumented revolute joints used to track the movement of the human arm. The exoskeleton provides arm weight support using two springs whose length can be adjusted using lead-screw sliders. One spring is used to unload the upper arm and one is used to unload the forearm. The system is utilized in rehabilitation to facilitate upper limb therapy. Subjects are prompted to perform motor tasks as they play video games that are controlled by moving the arm in space. Knowledge of results and performance are provided as video game score, task duration and angular displacements as measured via potentiometers positioned on the revolute joints. The relationship between angular displacements as measured by the potentiometers and the human arm joint kinematics needs to be determined to enable the clinical assessment of the patient’s arm movements during therapy administered using the ArmeoSpring system. To study such relationship, we collected data from 14 healthy volunteers while they performed the following single-joint movements: shoulder horizontal ab/adduction; shoulder frontal elevation; shoulder lateral elevation; elbow horizontal flexion/extension; and elbow vertical flexion/extension. The human arm joint kinematics was estimated using an eight-camera VICON system that captured 120 frames/s and the CAST protocol proposed by Cappozzo et al[i]. Clusters of markers were applied on the subject’s arm segments. Anatomical landmarks were calibrated to identify anatomical reference systems according to the ISB recommendations for reporting human joint motion. The joint kinematics of the human shoulder and of the elbow was estimated accordingly. The outputs of the exoskeleton potentiometers were recorded simultaneously to the motion capture data and used as input to a model that we developed to generate estimates of the human shoulder and elbow kinematics. The model was derived and tested by using the leave-one-out method. Results showed that the estimates of the human shoulder and elbow kinematics derived from the outputs of the exoskeleton potentiometers closely approximated the shoulder and elbow kinematics derived using the motion capture system.